

import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
A_		:      Any					    = logging.getLogger(__name__)


def    __a							( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE   )   ->  Optional[int]:






    '''simple docstring'''
    __UpperCAmelCase					       =      np.argmax(_lowerCamelCase							, axis=1   )
    return np.sum(outputs == labels   )


def    __a							( SCREAMING_SNAKE_CASE   )   ->  Any:






    '''simple docstring'''




    with open(_lowerCamelCase							, encoding='''utf_8'''   ) as f:
        __UpperCAmelCase					       =      csv.reader(_lowerCamelCase   )
        __UpperCAmelCase					       =      []
        next(_lowerCamelCase   )  # skip the first line
        for line in tqdm(_lowerCamelCase   ):
            output.append((''' '''.join(line[1:5]   ), line[5], line[6], int(line[-1]   ) - 1)   )
    return output


def    __a							( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE   )   ->  List[Any]:






    '''simple docstring'''
    __UpperCAmelCase					       =      []
    for dataset in encoded_datasets:
        __UpperCAmelCase					       =      len(_lowerCamelCase   )
        __UpperCAmelCase					       =      np.zeros((n_batch, 2, input_len)							, dtype=np.intaa   )
        __UpperCAmelCase					       =      np.zeros((n_batch, 2)							, dtype=np.intaa   )
        __UpperCAmelCase					       =      np.full((n_batch, 2, input_len)							, fill_value=-1_0_0							, dtype=np.intaa   )
        __UpperCAmelCase					       =      np.zeros((n_batch,)							, dtype=np.intaa   )
        for (
            i,
            (story, conta, conta, mc_label),
        ) in enumerate(_lowerCamelCase   ):
            __UpperCAmelCase					       =      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
            __UpperCAmelCase					       =      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
            __UpperCAmelCase					       =      with_conta
            __UpperCAmelCase					       =      with_conta
            __UpperCAmelCase					       =      len(_lowerCamelCase   ) - 1
            __UpperCAmelCase					       =      len(_lowerCamelCase   ) - 1
            __UpperCAmelCase					       =      with_conta
            __UpperCAmelCase					       =      with_conta
            __UpperCAmelCase					       =      mc_label
        __UpperCAmelCase					       =      (input_ids, mc_token_ids, lm_labels, mc_labels)
        tensor_datasets.append(tuple(torch.tensor(_lowerCamelCase   ) for t in all_inputs   )   )
    return tensor_datasets


def    __a							( )   ->  List[str]:






    '''simple docstring'''
    __UpperCAmelCase					       =      argparse.ArgumentParser()
    parser.add_argument('''--model_name'''							, type=_lowerCamelCase							, default='''openai-gpt'''							, help='''pretrained model name'''   )
    parser.add_argument('''--do_train'''							, action='''store_true'''							, help='''Whether to run training.'''   )
    parser.add_argument('''--do_eval'''							, action='''store_true'''							, help='''Whether to run eval on the dev set.'''   )
    parser.add_argument(
        '''--output_dir'''							, default=_lowerCamelCase							, type=_lowerCamelCase							, required=_lowerCamelCase							, help='''The output directory where the model predictions and checkpoints will be written.'''							, )
    parser.add_argument('''--train_dataset'''							, type=_lowerCamelCase							, default=''''''   )
    parser.add_argument('''--eval_dataset'''							, type=_lowerCamelCase							, default=''''''   )
    parser.add_argument('''--seed'''							, type=_lowerCamelCase							, default=4_2   )
    parser.add_argument('''--num_train_epochs'''							, type=_lowerCamelCase							, default=3   )
    parser.add_argument('''--train_batch_size'''							, type=_lowerCamelCase							, default=8   )
    parser.add_argument('''--eval_batch_size'''							, type=_lowerCamelCase							, default=1_6   )
    parser.add_argument('''--adam_epsilon'''							, default=1e-8							, type=_lowerCamelCase							, help='''Epsilon for Adam optimizer.'''   )
    parser.add_argument('''--max_grad_norm'''							, type=_lowerCamelCase							, default=1   )
    parser.add_argument(
        '''--max_steps'''							, default=-1							, type=_lowerCamelCase							, help=(
            '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
        )							, )
    parser.add_argument(
        '''--gradient_accumulation_steps'''							, type=_lowerCamelCase							, default=1							, help='''Number of updates steps to accumulate before                        performing a backward/update pass.'''							, )
    parser.add_argument('''--learning_rate'''							, type=_lowerCamelCase							, default=6.25e-5   )
    parser.add_argument('''--warmup_steps'''							, default=0							, type=_lowerCamelCase							, help='''Linear warmup over warmup_steps.'''   )
    parser.add_argument('''--lr_schedule'''							, type=_lowerCamelCase							, default='''warmup_linear'''   )
    parser.add_argument('''--weight_decay'''							, type=_lowerCamelCase							, default=0.01   )
    parser.add_argument('''--lm_coef'''							, type=_lowerCamelCase							, default=0.9   )
    parser.add_argument('''--n_valid'''							, type=_lowerCamelCase							, default=3_7_4   )

    parser.add_argument('''--server_ip'''							, type=_lowerCamelCase							, default=''''''							, help='''Can be used for distant debugging.'''   )
    parser.add_argument('''--server_port'''							, type=_lowerCamelCase							, default=''''''							, help='''Can be used for distant debugging.'''   )
    __UpperCAmelCase					       =      parser.parse_args()
    print(_lowerCamelCase   )

    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print('''Waiting for debugger attach'''   )
        ptvsd.enable_attach(address=(args.server_ip, args.server_port)							, redirect_output=_lowerCamelCase   )
        ptvsd.wait_for_attach()

    random.seed(args.seed   )
    np.random.seed(args.seed   )
    torch.manual_seed(args.seed   )
    torch.cuda.manual_seed_all(args.seed   )

    __UpperCAmelCase					       =      torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''   )
    __UpperCAmelCase					       =      torch.cuda.device_count()
    logger.info('''device: {}, n_gpu {}'''.format(_lowerCamelCase							, _lowerCamelCase   )   )

    if not args.do_train and not args.do_eval:
        raise ValueError('''At least one of `do_train` or `do_eval` must be True.'''   )

    if not os.path.exists(args.output_dir   ):
        os.makedirs(args.output_dir   )

    # Load tokenizer and model
    # This loading functions also add new tokens and embeddings called `special tokens`
    # These new embeddings will be fine-tuned on the RocStories dataset
    __UpperCAmelCase					       =      ['''_start_''', '''_delimiter_''', '''_classify_''']
    __UpperCAmelCase					       =      OpenAIGPTTokenizer.from_pretrained(args.model_name   )
    tokenizer.add_tokens(_lowerCamelCase   )
    __UpperCAmelCase					       =      tokenizer.convert_tokens_to_ids(_lowerCamelCase   )
    __UpperCAmelCase					       =      OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name   )
    model.resize_token_embeddings(len(_lowerCamelCase   )   )
    model.to(_lowerCamelCase   )

    # Load and encode the datasets
    def tokenize_and_encode(SCREAMING_SNAKE_CASE   ):
        if isinstance(_lowerCamelCase							, _lowerCamelCase   ):
            return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_lowerCamelCase   )   )
        elif isinstance(_lowerCamelCase							, _lowerCamelCase   ):
            return obj
        return [tokenize_and_encode(_lowerCamelCase   ) for o in obj]

    logger.info('''Encoding dataset...'''   )
    __UpperCAmelCase					       =      load_rocstories_dataset(args.train_dataset   )
    __UpperCAmelCase					       =      load_rocstories_dataset(args.eval_dataset   )
    __UpperCAmelCase					       =      (train_dataset, eval_dataset)
    __UpperCAmelCase					       =      tokenize_and_encode(_lowerCamelCase   )

    # Compute the max input length for the Transformer
    __UpperCAmelCase					       =      model.config.n_positions // 2 - 2
    __UpperCAmelCase					       =      max(
        len(story[:max_length]   ) + max(len(conta[:max_length]   )							, len(conta[:max_length]   )   ) + 3
        for dataset in encoded_datasets
        for story, conta, conta, _ in dataset   )
    __UpperCAmelCase					       =      min(_lowerCamelCase							, model.config.n_positions   )  # Max size of input for the pre-trained model

    # Prepare inputs tensors and dataloaders
    __UpperCAmelCase					       =      pre_process_datasets(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, *_lowerCamelCase   )
    __UpperCAmelCase       ,  __UpperCAmelCase					       =      tensor_datasets[0], tensor_datasets[1]

    __UpperCAmelCase					       =      TensorDataset(*_lowerCamelCase   )
    __UpperCAmelCase					       =      RandomSampler(_lowerCamelCase   )
    __UpperCAmelCase					       =      DataLoader(_lowerCamelCase							, sampler=_lowerCamelCase							, batch_size=args.train_batch_size   )

    __UpperCAmelCase					       =      TensorDataset(*_lowerCamelCase   )
    __UpperCAmelCase					       =      SequentialSampler(_lowerCamelCase   )
    __UpperCAmelCase					       =      DataLoader(_lowerCamelCase							, sampler=_lowerCamelCase							, batch_size=args.eval_batch_size   )

    # Prepare optimizer
    if args.do_train:
        if args.max_steps > 0:
            __UpperCAmelCase					       =      args.max_steps
            __UpperCAmelCase					       =      args.max_steps // (len(_lowerCamelCase   ) // args.gradient_accumulation_steps) + 1
        else:
            __UpperCAmelCase					       =      len(_lowerCamelCase   ) // args.gradient_accumulation_steps * args.num_train_epochs

        __UpperCAmelCase					       =      list(model.named_parameters()   )
        __UpperCAmelCase					       =      ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
        __UpperCAmelCase					       =      [
            {
                '''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay   )],
                '''weight_decay''': args.weight_decay,
            },
            {'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay   )], '''weight_decay''': 0.0},
        ]
        __UpperCAmelCase					       =      AdamW(_lowerCamelCase							, lr=args.learning_rate							, eps=args.adam_epsilon   )
        __UpperCAmelCase					       =      get_linear_schedule_with_warmup(
            _lowerCamelCase							, num_warmup_steps=args.warmup_steps							, num_training_steps=_lowerCamelCase   )

    if args.do_train:
        __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase					       =      0, 0, None
        model.train()
        for _ in trange(int(args.num_train_epochs   )							, desc='''Epoch'''   ):
            __UpperCAmelCase					       =      0
            __UpperCAmelCase					       =      0
            __UpperCAmelCase					       =      tqdm(_lowerCamelCase							, desc='''Training'''   )
            for step, batch in enumerate(_lowerCamelCase   ):
                __UpperCAmelCase					       =      tuple(t.to(_lowerCamelCase   ) for t in batch   )
                __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase					       =      batch
                __UpperCAmelCase					       =      model(_lowerCamelCase							, mc_token_ids=_lowerCamelCase							, lm_labels=_lowerCamelCase							, mc_labels=_lowerCamelCase   )
                __UpperCAmelCase					       =      args.lm_coef * losses[0] + losses[1]
                loss.backward()
                optimizer.step()
                scheduler.step()
                optimizer.zero_grad()
                tr_loss += loss.item()
                __UpperCAmelCase					       =      (
                    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                )
                nb_tr_steps += 1
                __UpperCAmelCase					       =      '''Training loss: {:.2e} lr: {:.2e}'''.format(_lowerCamelCase							, scheduler.get_lr()[0]   )

    # Save a trained model
    if args.do_train:
        # Save a trained model, configuration and tokenizer
        __UpperCAmelCase					       =      model.module if hasattr(_lowerCamelCase							, '''module'''   ) else model  # Only save the model itself

        # If we save using the predefined names, we can load using `from_pretrained`
        __UpperCAmelCase					       =      os.path.join(args.output_dir							, _lowerCamelCase   )
        __UpperCAmelCase					       =      os.path.join(args.output_dir							, _lowerCamelCase   )

        torch.save(model_to_save.state_dict()							, _lowerCamelCase   )
        model_to_save.config.to_json_file(_lowerCamelCase   )
        tokenizer.save_vocabulary(args.output_dir   )

        # Load a trained model and vocabulary that you have fine-tuned
        __UpperCAmelCase					       =      OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir   )
        __UpperCAmelCase					       =      OpenAIGPTTokenizer.from_pretrained(args.output_dir   )
        model.to(_lowerCamelCase   )

    if args.do_eval:
        model.eval()
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      0, 0
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      0, 0
        for batch in tqdm(_lowerCamelCase							, desc='''Evaluating'''   ):
            __UpperCAmelCase					       =      tuple(t.to(_lowerCamelCase   ) for t in batch   )
            __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase					       =      batch
            with torch.no_grad():
                __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase					       =      model(
                    _lowerCamelCase							, mc_token_ids=_lowerCamelCase							, lm_labels=_lowerCamelCase							, mc_labels=_lowerCamelCase   )

            __UpperCAmelCase					       =      mc_logits.detach().cpu().numpy()
            __UpperCAmelCase					       =      mc_labels.to('''cpu'''   ).numpy()
            __UpperCAmelCase					       =      accuracy(_lowerCamelCase							, _lowerCamelCase   )

            eval_loss += mc_loss.mean().item()
            eval_accuracy += tmp_eval_accuracy

            nb_eval_examples += input_ids.size(0   )
            nb_eval_steps += 1

        __UpperCAmelCase					       =      eval_loss / nb_eval_steps
        __UpperCAmelCase					       =      eval_accuracy / nb_eval_examples
        __UpperCAmelCase					       =      tr_loss / nb_tr_steps if args.do_train else None
        __UpperCAmelCase					       =      {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

        __UpperCAmelCase					       =      os.path.join(args.output_dir							, '''eval_results.txt'''   )
        with open(_lowerCamelCase							, '''w'''   ) as writer:
            logger.info('''***** Eval results *****'''   )
            for key in sorted(result.keys()   ):
                logger.info('''  %s = %s'''							, _lowerCamelCase							, str(result[key]   )   )
                writer.write('''%s = %s\n''' % (key, str(result[key]   ))   )


if __name__ == "__main__":
       main()






"""simple docstring"""






import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin



class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     DownBlockaD  # noqa F405
			lowercase        =     '''down'''






			def 					UpperCAmelCase							(self  :	int			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  [-0.02_32, -0.98_69, 0.80_54, -0.06_37, -0.16_88, -1.42_64, 0.44_70, -1.33_94, 0.09_04]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     ResnetDownsampleBlockaD  # noqa F405
			lowercase        =     '''down'''






			def 					UpperCAmelCase							(self  :	Any			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  [0.07_10, 0.24_10, -0.73_20, -1.07_57, -1.13_43, 0.35_40, -0.01_33, -0.25_76, 0.09_48]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     AttnDownBlockaD  # noqa F405
			lowercase        =     '''down'''






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  [0.06_36, 0.89_64, -0.62_34, -1.01_31, 0.08_44, 0.49_35, 0.34_37, 0.09_11, -0.29_57]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     CrossAttnDownBlockaD  # noqa F405
			lowercase        =     '''down'''






			def 					UpperCAmelCase							(self  :	int			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase							,       lowerCAmelCase												=  super().prepare_init_args_and_inputs_for_common()
				lowerCAmelCase												=  32
				return init_dict, inputs_dict






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  [0.22_38, -0.73_96, -0.22_55, -0.38_29, 0.19_25, 1.16_65, 0.06_03, -0.72_95, 0.19_83]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     SimpleCrossAttnDownBlockaD  # noqa F405
			lowercase        =     '''down'''






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				return super().get_dummy_input(include_encoder_hidden_states=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase							,       lowerCAmelCase												=  super().prepare_init_args_and_inputs_for_common()
				lowerCAmelCase												=  32
				return init_dict, inputs_dict






			@unittest.skipIf(torch_device == '''mps'''      ,'''MPS result is not consistent'''			)
			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  [0.79_21, -0.09_92, -0.19_62, -0.76_95, -0.42_42, 0.78_04, 0.47_37, 0.27_65, 0.33_38]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     SkipDownBlockaD  # noqa F405
			lowercase        =     '''down'''






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	int:





				"""simple docstring"""


				return super().get_dummy_input(include_skip_sample=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  [-0.08_45, -0.20_87, -0.24_65, 0.09_71, 0.19_00, -0.04_84, 0.26_64, 0.41_79, 0.50_69]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     AttnSkipDownBlockaD  # noqa F405
			lowercase        =     '''down'''






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Optional[int]:





				"""simple docstring"""


				return super().get_dummy_input(include_skip_sample=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  [0.55_39, 0.16_09, 0.49_24, 0.05_37, -0.19_95, 0.40_50, 0.09_79, -0.27_21, -0.06_42]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     DownEncoderBlockaD  # noqa F405
			lowercase        =     '''down'''






			@property
			def 					UpperCAmelCase							(self  :	int			)       ->	str:





				"""simple docstring"""


				return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	int			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  {
				    '''in_channels''': 32,
				    '''out_channels''': 32,
				}
				lowerCAmelCase												=  self.dummy_input
				return init_dict, inputs_dict






			def 					UpperCAmelCase							(self  :	str			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  [1.11_02, 0.53_02, 0.48_72, -0.00_23, -0.80_42, 0.04_83, -0.34_89, -0.56_32, 0.76_26]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     AttnDownEncoderBlockaD  # noqa F405
			lowercase        =     '''down'''






			@property
			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Union[str, Any]:





				"""simple docstring"""


				return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  {
				    '''in_channels''': 32,
				    '''out_channels''': 32,
				}
				lowerCAmelCase												=  self.dummy_input
				return init_dict, inputs_dict






			def 					UpperCAmelCase							(self  :	Any			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  [0.89_66, -0.14_86, 0.85_68, 0.81_41, -0.90_46, -0.13_42, -0.09_72, -0.74_17, 0.15_38]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     UNetMidBlockaD  # noqa F405
			lowercase        =     '''mid'''






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  {
				    '''in_channels''': 32,
				    '''temb_channels''': 128,
				}
				lowerCAmelCase												=  self.dummy_input
				return init_dict, inputs_dict






			def 					UpperCAmelCase							(self  :	Optional[Any]			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  [-0.10_62, 1.72_48, 0.34_94, 1.45_69, -0.09_10, -1.24_21, -0.99_84, 0.67_36, 1.00_28]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     UNetMidBlockaDCrossAttn  # noqa F405
			lowercase        =     '''mid'''






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase							,       lowerCAmelCase												=  super().prepare_init_args_and_inputs_for_common()
				lowerCAmelCase												=  32
				return init_dict, inputs_dict






			def 					UpperCAmelCase							(self  :	Any			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  [0.01_87, 2.42_20, 0.44_84, 1.12_03, -0.61_21, -1.51_22, -0.82_70, 0.78_51, 1.83_35]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     UNetMidBlockaDSimpleCrossAttn  # noqa F405
			lowercase        =     '''mid'''






			@property
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	str:





				"""simple docstring"""


				return super().get_dummy_input(include_encoder_hidden_states=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	int			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase							,       lowerCAmelCase												=  super().prepare_init_args_and_inputs_for_common()
				lowerCAmelCase												=  32
				return init_dict, inputs_dict






			def 					UpperCAmelCase							(self  :	str			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  [0.71_43, 1.99_74, 0.54_48, 1.39_77, 0.12_82, -1.12_37, -1.42_38, 0.55_30, 0.88_80]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     UpBlockaD  # noqa F405
			lowercase        =     '''up'''






			@property
			def 					UpperCAmelCase							(self  :	int			)       ->	Any:





				"""simple docstring"""


				return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  [-0.20_41, -0.41_65, -0.30_22, 0.00_41, -0.66_28, -0.70_53, 0.19_28, -0.03_25, 0.05_23]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     ResnetUpsampleBlockaD  # noqa F405
			lowercase        =     '''up'''






			@property
			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	str:





				"""simple docstring"""


				return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  [0.22_87, 0.35_49, -0.13_46, 0.47_97, -0.17_15, -0.96_49, 0.73_05, -0.58_64, -0.62_44]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     CrossAttnUpBlockaD  # noqa F405
			lowercase        =     '''up'''






			@property
			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	Optional[Any]:





				"""simple docstring"""


				return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase							,       lowerCAmelCase												=  super().prepare_init_args_and_inputs_for_common()
				lowerCAmelCase												=  32
				return init_dict, inputs_dict






			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  [-0.14_03, -0.35_15, -0.04_20, -0.14_25, 0.31_67, 0.50_94, -0.21_81, 0.59_31, 0.55_82]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     SimpleCrossAttnUpBlockaD  # noqa F405
			lowercase        =     '''up'''






			@property
			def 					UpperCAmelCase							(self  :	Any			)       ->	List[Any]:





				"""simple docstring"""


				return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_      ,include_encoder_hidden_states=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase							,       lowerCAmelCase												=  super().prepare_init_args_and_inputs_for_common()
				lowerCAmelCase												=  32
				return init_dict, inputs_dict






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  [0.26_45, 0.14_80, 0.09_09, 0.80_44, -0.97_58, -0.90_83, 0.09_94, -1.14_53, -0.74_02]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     AttnUpBlockaD  # noqa F405
			lowercase        =     '''up'''






			@property
			def 					UpperCAmelCase							(self  :	List[str]			)       ->	List[Any]:





				"""simple docstring"""


				return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_			)






			@unittest.skipIf(torch_device == '''mps'''      ,'''MPS result is not consistent'''			)
			def 					UpperCAmelCase							(self  :	int			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  [0.09_79, 0.13_26, 0.00_21, 0.06_59, 0.22_49, 0.00_59, 0.11_32, 0.59_52, 0.10_33]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     SkipUpBlockaD  # noqa F405
			lowercase        =     '''up'''






			@property
			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	List[str]:





				"""simple docstring"""


				return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  [-0.08_93, -0.12_34, -0.15_06, -0.03_32, 0.01_23, -0.02_11, 0.05_66, 0.01_43, 0.03_62]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     AttnSkipUpBlockaD  # noqa F405
			lowercase        =     '''up'''






			@property
			def 					UpperCAmelCase							(self  :	Optional[Any]			)       ->	str:





				"""simple docstring"""


				return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  [0.03_61, 0.06_17, 0.27_87, -0.03_50, 0.03_42, 0.34_21, -0.08_43, 0.09_13, 0.30_15]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     UpDecoderBlockaD  # noqa F405
			lowercase        =     '''up'''






			@property
			def 					UpperCAmelCase							(self  :	List[str]			)       ->	List[Any]:





				"""simple docstring"""


				return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  {'''in_channels''': 32, '''out_channels''': 32}

				lowerCAmelCase												=  self.dummy_input
				return init_dict, inputs_dict






			def 					UpperCAmelCase							(self  :	Dict			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  [0.44_04, 0.19_98, -0.98_86, -0.33_20, -0.31_28, -0.70_34, -0.69_55, -0.23_38, -0.31_37]
				super().test_output(SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     AttnUpDecoderBlockaD  # noqa F405
			lowercase        =     '''up'''






			@property
			def 					UpperCAmelCase							(self  :	Any			)       ->	Dict:





				"""simple docstring"""


				return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  {'''in_channels''': 32, '''out_channels''': 32}

				lowerCAmelCase												=  self.dummy_input
				return init_dict, inputs_dict






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  [0.67_38, 0.44_91, 0.10_55, 1.07_10, 0.73_16, 0.33_39, 0.33_52, 0.10_23, 0.35_68]
				super().test_output(SCREAMING_SNAKE_CASE_			)


